Machine Learning Datasets | Papers With Code

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class="d-md-none"> Gallery</span> </input> </label> </div> </div> <select class="custom-select custom-select-sm sort-order" name="o" onchange="this.form.submit()"> <option selected value="match">Best match</option> <option value="cited">Most cited</option> <option value="newest">Newest</option> </select> </form> <div id="id_datasets_filters" class="collapse collapse-md-show"> <div class="datasets-filter p-md-2 p-lg-3"> <div class="filter-name"> Filter by Modality <a class="clear-filter" href="?"> (clear) </a> </div> <div class="filter-items"> <a class="filter-item active" href="?page=1"> Graphs <span aria-hidden="true">×</span> </a> <a class="filter-item" href="?mod=images&page=1"> Images <span class="badge badge-light">2982</span> </a> <a class="filter-item" href="?mod=texts&page=1"> Texts <span class="badge badge-light">2866</span> </a> <a class="filter-item" href="?mod=videos&page=1"> Videos <span class="badge badge-light">940</span> </a> <a class="filter-item" href="?mod=audio&page=1"> Audio <span class="badge badge-light">451</span> </a> <a class="filter-item" href="?mod=medical&page=1"> Medical <span class="badge badge-light">369</span> </a> <a class="filter-item" href="?mod=3d&page=1"> 3D <span class="badge badge-light">348</span> </a> <a class="filter-item" href="?mod=time-series&page=1"> Time series <span class="badge badge-light">246</span> </a> <a class="filter-item" href="?mod=tabular&page=1"> Tabular <span class="badge badge-light">212</span> </a> <a class="filter-item" href="?mod=speech&page=1"> Speech <span class="badge badge-light">186</span> </a> <a class="filter-item" href="?mod=rgb-d&page=1"> RGB-D <span class="badge badge-light">177</span> </a> <a class="filter-item" href="?mod=environment&page=1"> Environment <span class="badge badge-light">139</span> </a> <a class="filter-item" href="?mod=point-cloud&page=1"> Point cloud <span class="badge badge-light">124</span> </a> <a class="filter-item" href="?mod=biomedical&page=1"> Biomedical <span class="badge badge-light">112</span> </a> <a class="filter-item" href="?mod=lidar&page=1"> LiDAR <span class="badge badge-light">85</span> </a> <a class="filter-item" href="?mod=rgb-video&page=1"> RGB Video <span class="badge badge-light">82</span> </a> <a class="filter-item" href="?mod=tracking&page=1"> Tracking <span class="badge badge-light">68</span> </a> <a class="filter-item" href="?mod=3d-meshes&page=1"> 3d meshes <span class="badge badge-light">59</span> </a> <a class="filter-item" href="?mod=biology&page=1"> Biology <span class="badge badge-light">59</span> </a> <a class="filter-item" href="?mod=actions&page=1"> Actions <span class="badge badge-light">56</span> </a> <a class="filter-item" href="?mod=tables&page=1"> Tables <span class="badge badge-light">46</span> </a> <a class="filter-item" href="?mod=stereo&page=1"> Stereo <span class="badge badge-light">43</span> </a> <a class="filter-item" href="?mod=eeg&page=1"> EEG <span class="badge badge-light">41</span> </a> <a class="filter-item" href="?mod=music&page=1"> Music <span class="badge badge-light">41</span> </a> <a class="filter-item" href="?mod=hyperspectral-images&page=1"> Hyperspectral images <span class="badge badge-light">40</span> </a> <a class="filter-item" href="?mod=mri&page=1"> MRI <span class="badge badge-light">36</span> </a> <a class="filter-item" href="?mod=interactive&page=1"> Interactive <span class="badge badge-light">28</span> </a> <a class="filter-item" href="?mod=physics&page=1"> Physics <span class="badge badge-light">27</span> </a> <a class="filter-item" href="?mod=dialog&page=1"> Dialog <span class="badge badge-light">24</span> </a> <a class="filter-item" href="?mod=midi&page=1"> Midi <span class="badge badge-light">21</span> </a> <a class="filter-item" href="?mod=6d&page=1"> 6D <span class="badge badge-light">15</span> </a> <a class="filter-item" href="?mod=ranking&page=1"> Ranking <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?mod=replay-data&page=1"> Replay data <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?mod=financial&page=1"> Financial <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?mod=fmri&page=1"> fMRI <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?mod=cad&page=1"> Cad <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?mod=parallel&page=1"> Parallel <span class="badge badge-light">6</span> </a> <a class="filter-item" href="?mod=lyrics&page=1"> Lyrics <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=psg&page=1"> PSG <span class="badge badge-light">2</span> </a> </div> </div> <div class="datasets-filter p-md-2 p-lg-3"> <div class="filter-name">Filter by Task </div> <div class="filter-items"> <a class="filter-item" href="?mod=graphs&task=node-classification&page=1"> Node Classification <span class="badge badge-light">59</span> </a> <a class="filter-item" href="?mod=graphs&task=link-prediction&page=1"> Link Prediction <span class="badge badge-light">41</span> </a> <a class="filter-item" href="?mod=graphs&task=graph-classification&page=1"> Graph Classification <span class="badge badge-light">30</span> </a> <a class="filter-item" href="?mod=graphs&task=node-classification-on-non-homophilic&page=1"> Node Classification on Non-Homophilic (Heterophilic) Graphs <span class="badge badge-light">15</span> </a> <a class="filter-item" href="?mod=graphs&task=knowledge-graphs&page=1"> Knowledge Graphs <span class="badge badge-light">12</span> </a> <a class="filter-item" href="?mod=graphs&task=graph-clustering&page=1"> Graph Clustering <span class="badge badge-light">11</span> </a> <a class="filter-item" href="?mod=graphs&task=knowledge-graph-completion&page=1"> Knowledge Graph Completion <span class="badge badge-light">11</span> </a> <a class="filter-item" href="?mod=graphs&task=node-clustering&page=1"> Node Clustering <span class="badge badge-light">11</span> </a> <a class="filter-item" href="?mod=graphs&task=community-detection&page=1"> Community Detection <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?mod=graphs&task=text-classification&page=1"> Text Classification <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?mod=graphs&task=graph-embedding&page=1"> Graph Embedding <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?mod=graphs&task=image-classification&page=1"> Image Classification <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?mod=graphs&task=recommendation-systems&page=1"> Recommendation Systems <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?mod=graphs&task=graph-regression&page=1"> Graph Regression <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?mod=graphs&task=amr-parsing&page=1"> AMR Parsing <span class="badge badge-light">5</span> </a> <a class="filter-item" href="?mod=graphs&task=amr-to-text-generation&page=1"> AMR-to-Text Generation <span class="badge badge-light">5</span> </a> <a class="filter-item" href="?mod=graphs&task=classification-1&page=1"> Classification <span class="badge badge-light">5</span> </a> <a class="filter-item" href="?mod=graphs&task=fake-news-detection&page=1"> Fake News Detection <span class="badge badge-light">5</span> </a> <a class="filter-item" href="?mod=graphs&task=graph-matching&page=1"> Graph Matching <span class="badge badge-light">5</span> </a> <a class="filter-item" href="?mod=graphs&task=graph-representation-learning&page=1"> Graph Representation Learning <span class="badge badge-light">5</span> </a> <a class="filter-item" href="?mod=graphs&task=language-modelling&page=1"> Language Modelling <span class="badge badge-light">5</span> </a> <a class="filter-item" href="?mod=graphs&task=question-answering&page=1"> Question Answering <span class="badge badge-light">5</span> </a> <a class="filter-item" href="?mod=graphs&task=anomaly-detection&page=1"> Anomaly Detection <span class="badge badge-light">4</span> </a> <a class="filter-item" href="?mod=graphs&task=fraud-detection&page=1"> Fraud Detection <span class="badge badge-light">4</span> </a> <a class="filter-item" href="?mod=graphs&task=graph-learning&page=1"> Graph Learning <span class="badge badge-light">4</span> </a> <a class="filter-item" href="?mod=graphs&task=graph-mining&page=1"> Graph Mining <span class="badge badge-light">4</span> </a> <a class="filter-item" href="?mod=graphs&task=knowledge-graph-embedding&page=1"> Knowledge Graph Embedding <span class="badge badge-light">4</span> </a> <a class="filter-item" href="?mod=graphs&task=misinformation&page=1"> Misinformation <span class="badge badge-light">4</span> </a> <a class="filter-item" href="?mod=graphs&task=network-embedding&page=1"> Network Embedding <span class="badge badge-light">4</span> </a> <a class="filter-item" href="?mod=graphs&task=relation-extraction&page=1"> Relation Extraction <span class="badge badge-light">4</span> </a> <a class="filter-item" href="?mod=graphs&task=text-summarization&page=1"> Text Summarization <span class="badge badge-light">4</span> </a> <a class="filter-item" href="?mod=graphs&task=clique-prediction&page=1"> Clique Prediction <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?mod=graphs&task=complex-query-answering&page=1"> Complex Query Answering <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?mod=graphs&task=document-classification&page=1"> Document Classification <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?mod=graphs&task=entity-alignment&page=1"> Entity Alignment <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?mod=graphs&task=feedback-vertex-set-fvs&page=1"> Feedback Vertex Set (FVS) <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?mod=graphs&task=graph-sampling&page=1"> Graph Sampling <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?mod=graphs&task=imputation&page=1"> Imputation <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?mod=graphs&task=kg-to-text&page=1"> KG-to-Text Generation <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?mod=graphs&task=multi-modal-entity-alignment&page=1"> Multi-modal Entity Alignment <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?mod=graphs&task=topological-data-analysis&page=1"> Topological Data Analysis <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?mod=graphs&task=ancestor-descendant-prediction&page=1"> Ancestor-descendant prediction <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=atomic-number-classification&page=1"> Atomic number classification <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=band-gap&page=1"> Band Gap <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=crystal-system-classification&page=1"> Crystal system classification <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=data-to-text-generation&page=1"> Data-to-Text Generation <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=distance-regression&page=1"> Distance regression <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=domain-adaptation&page=1"> Domain Adaptation <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=dynamic-link-prediction&page=1"> Dynamic Link Prediction <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=extended-summarization&page=1"> Extended Summarization <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=formation-energy&page=1"> Formation Energy <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=graph-generation&page=1"> Graph Generation <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=graph-similarity&page=1"> Graph Similarity <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=graph-structure-learning&page=1"> Graph structure learning <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=heterogeneous-node-classification&page=1"> Heterogeneous Node Classification <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=inductive-link-prediction&page=1"> Inductive Link Prediction <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=joint-entity-and-relation-extraction&page=1"> Joint Entity and Relation Extraction <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=knowledge-graph-embeddings&page=1"> Knowledge Graph Embeddings <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=link-sign-prediction&page=1"> Link Sign Prediction <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=metric-learning&page=1"> Metric Learning <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=multi-label-text-classification&page=1"> Multi-Label Text Classification <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=multi-modal-document-classification&page=1"> Multi-Modal Document Classification <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=multi-modal-knowledge-graph&page=1"> Multi-modal Knowledge Graph <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=nd-regression&page=1"> ND regression <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=named-entity-recognition-ner&page=1"> Named Entity Recognition (NER) <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=network-community-partition&page=1"> Network Community Partition <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=neutron-pdf-regression&page=1"> Neutron PDF regression <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=position-regression&page=1"> Position regression <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=quantization&page=1"> Quantization <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=reinforcement-learning-1&page=1"> Reinforcement Learning (RL) <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=relational-reasoning&page=1"> Relational Reasoning <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=sans-regression&page=1"> SANS regression <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=saxs-regression&page=1"> SAXS regression <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=semantic-parsing&page=1"> Semantic Parsing <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=space-group-classification&page=1"> Space group classification <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=stereo-matching-1&page=1"> Stereo Matching <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=text-generation&page=1"> Text Generation <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=text-retrieval&page=1"> Text Retrieval <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=topic-models&page=1"> Topic Models <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=trajectory-prediction&page=1"> Trajectory Prediction <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=x-ray-pdf-regression&page=1"> X-ray PDF regression <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=xrd-regression&page=1"> XRD regression <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=graphs&task=2d-object-detection&page=1"> 2D Object Detection <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=3d-classification&page=1"> 3D Classification <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=3d-human-pose-estimation&page=1"> 3D Human Pose Estimation <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=3d-instance-segmentation-1&page=1"> 3D Instance Segmentation <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=3d-object-detection&page=1"> 3D Object Detection <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=3d-semantic-segmentation&page=1"> 3D Semantic Segmentation <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=abstractive-text-summarization&page=1"> Abstractive Text Summarization <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=action-recognition-in-videos&page=1"> Action Recognition <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=active-learning&page=1"> Active Learning <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=air-pollution-prediction&page=1"> Air Pollution Prediction <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=anomaly-forecasting&page=1"> Anomaly Forecasting <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=automated-theorem-proving&page=1"> Automated Theorem Proving <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=autonomous-driving&page=1"> Autonomous Driving <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=autonomous-vehicles&page=1"> Autonomous Vehicles <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=bayesian-inference&page=1"> Bayesian Inference <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=covid-19-tracking&page=1"> COVID-19 Tracking <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=camera-localization&page=1"> Camera Localization <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=causal-discovery&page=1"> Causal Discovery <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=citation-recommendation&page=1"> Citation Recommendation <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=code-completion&page=1"> Code Completion <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=collaborative-filtering&page=1"> Collaborative Filtering <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=combinatorial-optimization&page=1"> Combinatorial Optimization <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=common-sense-reasoning&page=1"> Common Sense Reasoning <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=conditional-text-generation&page=1"> Conditional Text Generation <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=continual-learning&page=1"> Continual Learning <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=conversational-response-selection&page=1"> Conversational Response Selection <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=coreference-resolution&page=1"> Coreference Resolution <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=cross-modal-retrieval&page=1"> Cross-Modal Retrieval <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=cross-view-geo-localisation&page=1"> Cross-View Geo-Localisation <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=data-augmentation&page=1"> Data Augmentation <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=demand-forecasting&page=1"> Demand Forecasting <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=dialogue-evaluation&page=1"> Dialogue Evaluation <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=dialogue-generation&page=1"> Dialogue Generation <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=dimensionality-reduction&page=1"> Dimensionality Reduction <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=document-summarization&page=1"> Document Summarization <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=edge-classification&page=1"> Edge Classification <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=entity-embeddings&page=1"> Entity Embeddings <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=entity-resolution&page=1"> Entity Resolution <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=entity-typing&page=1"> Entity Typing <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=explanation-fidelity-evaluation&page=1"> Explanation Fidelity Evaluation <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=face-clustering&page=1"> Face Clustering <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=factual-visual-question-answering&page=1"> Factual Visual Question Answering <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=few-shot-learning&page=1"> Few-Shot Learning <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=gait-recognition&page=1"> Gait Recognition <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=gene-interaction-prediction&page=1"> Gene Interaction Prediction <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=generative-question-answering&page=1"> Generative Question Answering <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=graph-property-prediction&page=1"> Graph Property Prediction <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=graph-reconstruction&page=1"> Graph Reconstruction <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=graph-to-sequence&page=1"> Graph-to-Sequence <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=graphon-estimation&page=1"> Graphon Estimation <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=hate-speech-detection&page=1"> Hate Speech Detection <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=hypergraph-contrastive-learning&page=1"> Hypergraph Contrastive Learning <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=image-captioning&page=1"> Image Captioning <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=image-retrieval&page=1"> Image Retrieval <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=image-based-localization&page=1"> Image-Based Localization <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=indoor-scene-reconstruction&page=1"> Indoor Scene Reconstruction <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=indoor-scene-synthesis&page=1"> Indoor Scene Synthesis <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=inductive-logic-programming&page=1"> Inductive logic programming <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=intelligent-communication&page=1"> Intelligent Communication <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=joint-entity-and-relation-extraction-on&page=1"> Joint Entity and Relation Extraction on Scientific Data <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=knowledge-base-completion&page=1"> Knowledge Base Completion <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=knowledge-aware-recommendation&page=1"> Knowledge-Aware Recommendation <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=layout-design&page=1"> Layout Design <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=link-property-prediction&page=1"> Link Property Prediction <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=malware-detection&page=1"> Malware Detection <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=malware-family-detection&page=1"> Malware Family Detection <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=malware-type-detection&page=1"> Malware Type Detection <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=materials-screening&page=1"> Materials Screening <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=medical-diagnosis&page=1"> Medical Diagnosis <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=medical-named-entity-recognition&page=1"> Medical Named Entity Recognition <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=medical-relation-extraction&page=1"> Medical Relation Extraction <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=molecular-graph-generation&page=1"> Molecular Graph Generation <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=molecular-property-prediction&page=1"> Molecular Property Prediction <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=molecule-captioning&page=1"> Molecule Captioning <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=motion-forecasting&page=1"> Motion Forecasting <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=movie-recommendation&page=1"> Movie Recommendation <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=multi-label-classification&page=1"> Multi-Label Classification <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=multi-agent-reinforcement-learning&page=1"> Multi-agent Reinforcement Learning <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=cg&page=1"> NER <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=network-pruning&page=1"> Network Pruning <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=news-generation&page=1"> News Generation <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=news-recommendation&page=1"> News Recommendation <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=node-property-prediction&page=1"> Node Property Prediction <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=open-intent-discovery&page=1"> Open Intent Discovery <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=open-domain-dialog&page=1"> Open-Domain Dialog <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=outdoor-localization&page=1"> Outdoor Localization <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=outlier-detection&page=1"> Outlier Detection <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=parameter-prediction&page=1"> Parameter Prediction <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=persian-sentiment-anlysis&page=1"> Persian Sentiment Analysis <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=physical-simulations&page=1"> Physical Simulations <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=plan2scene&page=1"> Plan2Scene <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=predictive-process-monitoring&page=1"> Predictive Process Monitoring <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=product-categorization&page=1"> Product Categorization <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=product-recommendation&page=1"> Product Recommendation <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=product-relation-classification&page=1"> Product Relation Classification <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=product-relation-detection&page=1"> Product Relation Detection <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=production-forecasting&page=1"> Production Forecasting <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=program-synthesis&page=1"> Program Synthesis <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=protein-structure-prediction&page=1"> Protein Structure Prediction <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=q-learning&page=1"> Q-Learning <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=reading-comprehension&page=1"> Reading Comprehension <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=relation-classification&page=1"> Relation Classification <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=remote-sensing-image-classification&page=1"> Remote Sensing Image Classification <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=research-performance-prediction&page=1"> Research Performance Prediction <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=retrieval&page=1"> Retrieval <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=review-generation&page=1"> Review Generation <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=roomenv-v0&page=1"> RoomEnv-v0 <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=roomenv-v1&page=1"> RoomEnv-v1 <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=roomenv-v2&page=1"> RoomEnv-v2 <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=sarcasm-detection&page=1"> Sarcasm Detection <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=scene-graph-generation&page=1"> Scene Graph Generation <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=science-question-answering&page=1"> Science Question Answering <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=scientific-concept-extraction&page=1"> Scientific Concept Extraction <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=scientific-article-summarization&page=1"> Scientific Document Summarization <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=self-driving-cars&page=1"> Self-Driving Cars <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=semantic-role-labeling&page=1"> Semantic Role Labeling <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=semi-supervised-image-classification&page=1"> Semi-Supervised Image Classification <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=semi-supervised-text-classification-1&page=1"> Semi-Supervised Text Classification <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=sentence-classification&page=1"> Sentence Classification <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=sentiment-analysis&page=1"> Sentiment Analysis <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=sentiment-classification&page=1"> Sentiment Classification <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=sequence-to-sequence-language-modeling&page=1"> Sequence-to-sequence Language Modeling <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=small-data&page=1"> Small Data Image Classification <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=steiner-tree-problem&page=1"> Steiner Tree Problem <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=stochastic-optimization&page=1"> Stochastic Optimization <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=supervised-text-retrieval&page=1"> Supervised Text Retrieval <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=temporal-relation-classification&page=1"> Temporal Relation Classification <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=text-based-de-novo-molecule-generation&page=1"> Text-based de novo Molecule Generation <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=time-series-forecasting&page=1"> Time Series Forecasting <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=time-interval-prediction&page=1"> Time-interval Prediction <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=topic-classification&page=1"> Topic Classification <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=total-magnetization&page=1"> Total Magnetization <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=travel-time-estimation&page=1"> Travel Time Estimation <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=traveling-salesman-problem&page=1"> Traveling Salesman Problem <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=triple-classification&page=1"> Triple Classification <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=unsupervised-anomaly-detection&page=1"> Unsupervised Anomaly Detection <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=unsupervised-extractive-summarization&page=1"> Unsupervised Extractive Summarization <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=unsupervised-kg-to-text-generation&page=1"> Unsupervised KG-to-Text Generation <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=video-classification&page=1"> Video Classification <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=visual-localization&page=1"> Visual Localization <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=visual-place-recognition&page=1"> Visual Place Recognition <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=visual-question-answering&page=1"> Visual Question Answering (VQA) <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=visual-reasoning&page=1"> Visual 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motion prediction <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=regression-1&page=1"> regression <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=tabular-regression&page=1"> tabular-regression <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?mod=graphs&task=text-based-games&page=1"> text-based games <span class="badge badge-light">1</span> </a> </div> </div> <div class="datasets-filter p-md-2 p-lg-3"> <div class="filter-name"> Filter by Language </div> <div class="filter-items"> <a class="filter-item" href="?mod=graphs&lang=english&page=1"> English <span class="badge badge-light">135</span> </a> <a class="filter-item" href="?mod=graphs&lang=chinese&page=1"> Chinese <span class="badge badge-light">5</span> </a> <a class="filter-item" href="?mod=graphs&lang=french&page=1"> French <span class="badge badge-light">4</span> </a> <a class="filter-item" 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class="filter-item" href="?mod=graphs&lang=rajasthani&page=1"> Rajasthani <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?mod=graphs&lang=romanian&page=1"> Romanian <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?mod=graphs&lang=romansh&page=1"> Romansh <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?mod=graphs&lang=rundi&page=1"> Rundi <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?mod=graphs&lang=russia-buriat&page=1"> Russia Buriat <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?mod=graphs&lang=rusyn&page=1"> Rusyn <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?mod=graphs&lang=saidi-arabic&page=1"> Saidi Arabic <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?mod=graphs&lang=samoan&page=1"> Samoan <span class="badge badge-light">0</span> </a> <a class="filter-item" 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href="?mod=graphs&lang=shan&page=1"> Shan <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?mod=graphs&lang=shona&page=1"> Shona <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?mod=graphs&lang=sichuan-yi&page=1"> Sichuan Yi <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?mod=graphs&lang=sicilian&page=1"> Sicilian <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?mod=graphs&lang=silesian&page=1"> Silesian <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?mod=graphs&lang=sindhi&page=1"> Sindhi <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?mod=graphs&lang=sinhala&page=1"> Sinhala <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?mod=graphs&lang=skolt-sami&page=1"> Skolt Sami <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?mod=graphs&lang=slovenian&page=1"> Slovenian <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?mod=graphs&lang=soi&page=1"> Soi <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?mod=graphs&lang=somali&page=1"> Somali <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?mod=graphs&lang=south-azerbaijani&page=1"> South Azerbaijani <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?mod=graphs&lang=south-levantine-arabic&page=1"> South Levantine Arabic <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?mod=graphs&lang=southern-pashto&page=1"> Southern Pashto <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?mod=graphs&lang=southern-sotho&page=1"> Southern Sotho <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?mod=graphs&lang=sranan-tongo&page=1"> Sranan Tongo <span class="badge badge-light">0</span> </a> <a class="filter-item" 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class="badge badge-light">0</span> </a> </div> </div> </div> </div> </div> <div class=" col-md-9"> <h1 class="results-count">260 dataset results for <span class="results-filter-names"> Graphs <a class="clear-filter-title" href="/datasets"> <span class=" icon-wrapper icon-fa icon-fa-regular" data-name="times"><svg viewBox="0 0 320.002 513.795" xmlns="http://www.w3.org/2000/svg"><path d="M207.6 256.98l107.73 107.71c6.23 6.24 6.23 16.35 0 22.58L290.3 412.3c-6.24 6.23-16.35 6.23-22.58 0L160 304.579 52.29 412.309c-6.24 6.23-16.35 6.23-22.58 0l-25.03-25.03c-6.23-6.24-6.23-16.35 0-22.58l107.72-107.72L4.68 149.258c-6.23-6.24-6.23-16.351 0-22.58l25.02-25.021c6.24-6.23 16.35-6.23 22.58 0L160 209.378l107.71-107.73c6.24-6.229 16.35-6.229 22.58 0l25.03 25.031c6.23 6.24 6.23 16.35 0 22.58z"/></svg></span> </a> </span> </h1> <div class="datasets-items show-list"> <div class="dataset-wide-box"> <a href="/dataset/pubmed"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-color: #90B06D;opacity: 0.3;"> <span class="dataset-img-icon"><span class=" icon-wrapper icon-fa icon-fa-duotone" data-name=""></span></span> <span class="dataset-img-text">Pubmed</span> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> Pubmed </span> <div class="description"> <p> The PubMed dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. </p> <p class="description-stats"> 1,159 <span class="smaller-text">PAPERS</span> • 24 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/ogb"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000005078-5b5b1cd9_oLn0Sj5.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> OGB <span class="full-name">(Open Graph Benchmark)</span> </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000005078-e8e291b8.jpg"> <p> The Open Graph Benchmark (OGB) is a collection of realistic, large-scale, and diverse benchmark datasets for machine learning on graphs. OGB datasets are automatically downloaded, processed, and split using the OGB Data Loader. The model performance can be evaluated using the OGB Evaluator in a unified manner. OGB is a community-driven initiative in active development. </p> <p class="description-stats"> 931 <span class="smaller-text">PAPERS</span> • 16 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/reddit"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000001419-f6818660_49gvISv.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> Reddit </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000001419-bc78ad74.jpg"> <p> The Reddit dataset is a graph dataset from Reddit posts made in the month of September, 2014. The node label in this case is the community, or “subreddit”, that a post belongs to. 50 large communities have been sampled to build a post-to-post graph, connecting posts if the same user comments on both. In total this dataset contains 232,965 posts with an average degree of 492. The first 20 days are used for training and the remaining days for testing (with 30% used for validation). For features, off-the-shelf 300-dimensional GloVe CommonCrawl word vectors are used. </p> <p class="description-stats"> 651 <span class="smaller-text">PAPERS</span> • 13 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/fb15k"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000001664-672abbcc_GT3TytA.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> FB15k <span class="full-name">(Freebase 15K)</span> </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000001664-1c8320a0.jpg"> <p> The FB15k dataset contains knowledge base relation triples and textual mentions of Freebase entity pairs. It has a total of 592,213 triplets with 14,951 entities and 1,345 relationships. FB15K-237 is a variant of the original dataset where inverse relations are removed, since it was found that a large number of test triplets could be obtained by inverting triplets in the training set. </p> <p class="description-stats"> 607 <span class="smaller-text">PAPERS</span> • 9 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/dbpedia"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000000317-f73dcc0a_oH76qdn.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> DBpedia </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000000317-dc3ad976.jpg"> <p> DBpedia (from "DB" for "database") is a project aiming to extract structured content from the information created in the Wikipedia project. DBpedia allows users to semantically query relationships and properties of Wikipedia resources, including links to other related datasets. </p> <p class="description-stats"> 577 <span class="smaller-text">PAPERS</span> • 4 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/cora"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000000700-fc96b306_r4h6Zl5.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> Cora </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000000700-71df6c02.jpg"> <p> The Cora dataset consists of 2708 scientific publications classified into one of seven classes. The citation network consists of 5429 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 1433 unique words. </p> <p class="description-stats"> 554 <span class="smaller-text">PAPERS</span> • 20 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/wn18"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-color: #90B06D;opacity: 0.3;"> <span class="dataset-img-icon"><span class=" icon-wrapper icon-fa icon-fa-duotone" data-name=""></span></span> <span class="dataset-img-text">WN18</span> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> WN18 <span class="full-name">(WordNet18)</span> </span> <div class="description"> <p> The WN18 dataset has 18 relations scraped from WordNet for roughly 41,000 synsets, resulting in 141,442 triplets. It was found out that a large number of the test triplets can be found in the training set with another relation or the inverse relation. Therefore, a new version of the dataset WN18RR has been proposed to address this issue. </p> <p class="description-stats"> 461 <span class="smaller-text">PAPERS</span> • 5 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/framenet"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000003531-8ac0613b_wy2uzyG.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> FrameNet </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000003531-1d021ec4.jpg"> <p> FrameNet is a linguistic knowledge graph containing information about lexical and predicate argument semantics of the English language. FrameNet contains two distinct entity classes: frames and lexical units, where a frame is a meaning and a lexical unit is a single meaning for a word. </p> <p class="description-stats"> 439 <span class="smaller-text">PAPERS</span> • <span class="smaller-text">NO BENCHMARKS YET</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/fb15k-237"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-color: #90B06D;opacity: 0.3;"> <span class="dataset-img-icon"><span class=" icon-wrapper icon-fa icon-fa-duotone" data-name=""></span></span> <span class="dataset-img-text">FB15k-237</span> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> FB15k-237 </span> <div class="description"> <p> FB15k-237 is a link prediction dataset created from FB15k. While FB15k consists of 1,345 relations, 14,951 entities, and 592,213 triples, many triples are inverses that cause leakage from the training to testing and validation splits. FB15k-237 was created by Toutanova and Chen (2015) to ensure that the testing and evaluation datasets do not have inverse relation test leakage. In summary, FB15k-237 dataset contains 310,116 triples with 14,541 entities and 237 relation types. </p> <p class="description-stats"> 433 <span class="smaller-text">PAPERS</span> • 3 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/wn18rr"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-color: #90B06D;opacity: 0.3;"> <span class="dataset-img-icon"><span class=" icon-wrapper icon-fa icon-fa-duotone" data-name=""></span></span> <span class="dataset-img-text">WN18RR</span> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> WN18RR </span> <div class="description"> <p> WN18RR is a link prediction dataset created from WN18, which is a subset of WordNet. WN18 consists of 18 relations and 40,943 entities. However, many text triples are obtained by inverting triples from the training set. Thus the WN18RR dataset is created to ensure that the evaluation dataset does not have inverse relation test leakage. In summary, WN18RR dataset contains 93,003 triples with 40,943 entities and 11 relation types. </p> <p class="description-stats"> 370 <span class="smaller-text">PAPERS</span> • 3 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/citeseer"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-color: #90B06D;opacity: 0.3;"> <span class="dataset-img-icon"><span class=" icon-wrapper icon-fa icon-fa-duotone" data-name=""></span></span> <span class="dataset-img-text">Citeseer</span> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> Citeseer </span> <div class="description"> <p> The CiteSeer dataset consists of 3312 scientific publications classified into one of six classes. The citation network consists of 4732 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 3703 unique words. </p> <p class="description-stats"> 354 <span class="smaller-text">PAPERS</span> • 14 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/yago"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000003525-493b6991_OPISADQ.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> YAGO <span class="full-name">(Yet Another Great Ontology)</span> </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000003525-3ba5f889.jpg"> <p> Yet Another Great Ontology (YAGO) is a Knowledge Graph that augments WordNet with common knowledge facts extracted from Wikipedia, converting WordNet from a primarily linguistic resource to a common knowledge base. YAGO originally consisted of more than 1 million entities and 5 million facts describing relationships between these entities. YAGO2 grounded entities, facts, and events in time and space, contained 446 million facts about 9.8 million entities, while YAGO3 added about 1 million more entities from non-English Wikipedia articles. YAGO3-10 a subset of YAGO3, containing entities which have a minimum of 10 relations each. </p> <p class="description-stats"> 348 <span class="smaller-text">PAPERS</span> • 7 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/proteins"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-color: #90B06D;opacity: 0.3;"> <span class="dataset-img-icon"><span class=" icon-wrapper icon-fa icon-fa-duotone" data-name=""></span></span> <span class="dataset-img-text">PROTEINS</span> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> PROTEINS </span> <div class="description"> <p> PROTEINS is a dataset of proteins that are classified as enzymes or non-enzymes. Nodes represent the amino acids and two nodes are connected by an edge if they are less than 6 Angstroms apart. </p> <p class="description-stats"> 345 <span class="smaller-text">PAPERS</span> • 1 <span class="smaller-text">BENCHMARK</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/imdb-binary"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-color: #90B06D;opacity: 0.3;"> <span class="dataset-img-icon"><span class=" icon-wrapper icon-fa icon-fa-duotone" data-name=""></span></span> <span class="dataset-img-text">IMDB-BINARY</span> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> IMDB-BINARY </span> <div class="description"> <p> IMDB-BINARY is a movie collaboration dataset that consists of the ego-networks of 1,000 actors/actresses who played roles in movies in IMDB. In each graph, nodes represent actors/actress, and there is an edge between them if they appear in the same movie. These graphs are derived from the Action and Romance genres. </p> <p class="description-stats"> 306 <span class="smaller-text">PAPERS</span> • 2 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/mutag"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-color: #90B06D;opacity: 0.3;"> <span class="dataset-img-icon"><span class=" icon-wrapper icon-fa icon-fa-duotone" data-name=""></span></span> <span class="dataset-img-text">MUTAG</span> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> MUTAG </span> <div class="description"> <p> In particular, MUTAG is a collection of nitroaromatic compounds and the goal is to predict their mutagenicity on Salmonella typhimurium. Input graphs are used to represent chemical compounds, where vertices stand for atoms and are labeled by the atom type (represented by one-hot encoding), while edges between vertices represent bonds between the corresponding atoms. It includes 188 samples of chemical compounds with 7 discrete node labels. </p> <p class="description-stats"> 262 <span class="smaller-text">PAPERS</span> • 3 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/collab"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-color: #90B06D;opacity: 0.3;"> <span class="dataset-img-icon"><span class=" icon-wrapper icon-fa icon-fa-duotone" data-name=""></span></span> <span class="dataset-img-text">COLLAB</span> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> COLLAB </span> <div class="description"> <p> COLLAB is a scientific collaboration dataset. A graph corresponds to a researcher’s ego network, i.e., the researcher and its collaborators are nodes and an edge indicates collaboration between two researchers. A researcher’s ego network has three possible labels, i.e., High Energy Physics, Condensed Matter Physics, and Astro Physics, which are the fields that the researcher belongs to. The dataset has 5,000 graphs and each graph has label 0, 1, or 2. </p> <p class="description-stats"> 248 <span class="smaller-text">PAPERS</span> • 2 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/imdb-multi"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-color: #90B06D;opacity: 0.3;"> <span class="dataset-img-icon"><span class=" icon-wrapper icon-fa icon-fa-duotone" data-name=""></span></span> <span class="dataset-img-text">IMDB-MULTI</span> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> IMDB-MULTI </span> <div class="description"> <p> IMDB-MULTI is a relational dataset that consists of a network of 1000 actors or actresses who played roles in movies in IMDB. A node represents an actor or actress, and an edge connects two nodes when they appear in the same movie. In IMDB-MULTI, the edges are collected from three different genres: Comedy, Romance and Sci-Fi. </p> <p class="description-stats"> 238 <span class="smaller-text">PAPERS</span> • 2 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/nci1"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-color: #90B06D;opacity: 0.3;"> <span class="dataset-img-icon"><span class=" icon-wrapper icon-fa icon-fa-duotone" data-name=""></span></span> <span class="dataset-img-text">NCI1</span> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> NCI1 </span> <div class="description"> <p> The NCI1 dataset comes from the cheminformatics domain, where each input graph is used as representation of a chemical compound: each vertex stands for an atom of the molecule, and edges between vertices represent bonds between atoms. This dataset is relative to anti-cancer screens where the chemicals are assessed as positive or negative to cell lung cancer. Each vertex has an input label representing the corresponding atom type, encoded by a one-hot-encoding scheme into a vector of 0/1 elements. </p> <p class="description-stats"> 238 <span class="smaller-text">PAPERS</span> • 2 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/moleculenet"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000007566-a28cc3be.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> MoleculeNet </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000007566-a3b342d2.jpg"> <p> MoleculeNet is a large scale benchmark for molecular machine learning. MoleculeNet curates multiple public datasets, establishes metrics for evaluation, and offers high quality open-source implementations of multiple previously proposed molecular featurization and learning algorithms (released as part of the DeepChem open source library). MoleculeNet benchmarks demonstrate that learnable representations are powerful tools for molecular machine learning and broadly offer the best performance. </p> <p class="description-stats"> 218 <span class="smaller-text">PAPERS</span> • 1 <span class="smaller-text">BENCHMARK</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/dblp"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-color: #90B06D;opacity: 0.3;"> <span class="dataset-img-icon"><span class=" icon-wrapper icon-fa icon-fa-duotone" data-name=""></span></span> <span class="dataset-img-text">DBLP</span> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> DBLP <span class="full-name">(Citation Network Dataset)</span> </span> <div class="description"> <p> The DBLP is a citation network dataset. The citation data is extracted from DBLP, ACM, MAG (Microsoft Academic Graph), and other sources. The first version contains 629,814 papers and 632,752 citations. Each paper is associated with abstract, authors, year, venue, and title. The data set can be used for clustering with network and side information, studying influence in the citation network, finding the most influential papers, topic modeling analysis, etc. </p> <p class="description-stats"> 208 <span class="smaller-text">PAPERS</span> • 5 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/wiki-squirrel"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000008181-ee0696b0.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> Wiki Squirrel <span class="full-name">(Wikipedia Squirrel)</span> </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000008181-ea000f00.jpg"> <p> The data was collected from the English Wikipedia (December 2018). These datasets represent page-page networks on specific topics (chameleons, crocodiles and squirrels). Nodes represent articles and edges are mutual links between them. The edges csv files contain the edges - nodes are indexed from 0. The features json files contain the features of articles - each key is a page id, and node features are given as lists. The presence of a feature in the feature list means that an informative noun appeared in the text of the Wikipedia article. The target csv contains the node identifiers and the average monthly traffic between October 2017 and November 2018 for each page. For each page-page network we listed the number of nodes an edges with some other descriptive statistics. </p> <p class="description-stats"> 193 <span class="smaller-text">PAPERS</span> • 2 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/enzymes"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-color: #90B06D;opacity: 0.3;"> <span class="dataset-img-icon"><span class=" icon-wrapper icon-fa icon-fa-duotone" data-name=""></span></span> <span class="dataset-img-text">ENZYMES</span> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> ENZYMES </span> <div class="description"> <p> ENZYMES is a dataset of 600 protein tertiary structures obtained from the BRENDA enzyme database. The ENZYMES dataset contains 6 enzymes. </p> <p class="description-stats"> 185 <span class="smaller-text">PAPERS</span> • 1 <span class="smaller-text">BENCHMARK</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/snap"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000003377-cdc3a3ab_3bcT4F2.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> SNAP <span class="full-name">(Stanford Large Network Dataset Collection)</span> </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000003377-d1d81e4d.jpg"> <p> SNAP is a collection of large network datasets. It includes graphs representing social networks, citation networks, web graphs, online communities, online reviews and more. </p> <p class="description-stats"> 162 <span class="smaller-text">PAPERS</span> • <span class="smaller-text">NO BENCHMARKS YET</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/cluster"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-color: #90B06D;opacity: 0.3;"> <span class="dataset-img-icon"><span class=" icon-wrapper icon-fa icon-fa-duotone" data-name=""></span></span> <span class="dataset-img-text">CLUSTER</span> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> CLUSTER </span> <div class="description"> <p> CLUSTER is a node classification tasks generated with Stochastic Block Models, which is widely used to model communities in social networks by modulating the intra- and extra-communities connections, thereby controlling the difficulty of the task. CLUSTER aims at identifying community clusters in a semi-supervised setting. </p> <p class="description-stats"> 152 <span class="smaller-text">PAPERS</span> • 1 <span class="smaller-text">BENCHMARK</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/reddit-binary"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-color: #90B06D;opacity: 0.3;"> <span class="dataset-img-icon"><span class=" icon-wrapper icon-fa icon-fa-duotone" data-name=""></span></span> <span class="dataset-img-text">REDDIT-BINARY</span> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> REDDIT-BINARY </span> <div class="description"> <p> REDDIT-BINARY consists of graphs corresponding to online discussions on Reddit. In each graph, nodes represent users, and there is an edge between them if at least one of them respond to the other’s comment. There are four popular subreddits, namely, IAmA, AskReddit, TrollXChromosomes, and atheism. IAmA and AskReddit are two question/answer based subreddits, and TrollXChromosomes and atheism are two discussion-based subreddits. A graph is labeled according to whether it belongs to a question/answer-based community or a discussion-based community. </p> <p class="description-stats"> 149 <span class="smaller-text">PAPERS</span> • 2 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/pattern"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-color: #90B06D;opacity: 0.3;"> <span class="dataset-img-icon"><span class=" icon-wrapper icon-fa icon-fa-duotone" data-name=""></span></span> <span class="dataset-img-text">PATTERN</span> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> PATTERN </span> <div class="description"> <p> PATTERN is a node classification tasks generated with Stochastic Block Models, which is widely used to model communities in social networks by modulating the intra- and extra-communities connections, thereby controlling the difficulty of the task. PATTERN tests the fundamental graph task of recognizing specific predetermined subgraphs. </p> <p class="description-stats"> 138 <span class="smaller-text">PAPERS</span> • 1 <span class="smaller-text">BENCHMARK</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/materials-project"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-color: #90B06D;opacity: 0.3;"> <span class="dataset-img-icon"><span class=" icon-wrapper icon-fa icon-fa-duotone" data-name=""></span></span> <span class="dataset-img-text">Materials Project</span> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> Materials Project </span> <div class="description"> <p> The Materials Project is a collection of chemical compounds labelled with different attributes. The labelling is performed by different simulations, most of them at DFT level of theory. </p> <p class="description-stats"> 133 <span class="smaller-text">PAPERS</span> • 2 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/ptc"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-color: #90B06D;opacity: 0.3;"> <span class="dataset-img-icon"><span class=" icon-wrapper icon-fa icon-fa-duotone" data-name=""></span></span> <span class="dataset-img-text">PTC</span> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> PTC <span class="full-name">(Predictive Toxicology Challenge)</span> </span> <div class="description"> <p> PTC is a collection of 344 chemical compounds represented as graphs which report the carcinogenicity for rats. There are 19 node labels for each node. </p> <p class="description-stats"> 106 <span class="smaller-text">PAPERS</span> • 1 <span class="smaller-text">BENCHMARK</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/webkb"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-color: #90B06D;opacity: 0.3;"> <span class="dataset-img-icon"><span class=" icon-wrapper icon-fa icon-fa-duotone" data-name=""></span></span> <span class="dataset-img-text">WebKB</span> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> WebKB </span> <div class="description"> <p> WebKB is a dataset that includes web pages from computer science departments of various universities. 4,518 web pages are categorized into 6 imbalanced categories (Student, Faculty, Staff, Department, Course, Project). Additionally there is Other miscellanea category that is not comparable to the rest. </p> <p class="description-stats"> 103 <span class="smaller-text">PAPERS</span> • 6 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/wiki-cs"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000004098-6c891896_jTedFg6.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> Wiki-CS </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000004098-52500e4a.jpg"> <p> Wiki-CS is a Wikipedia-based dataset for benchmarking Graph Neural Networks. The dataset is constructed from Wikipedia categories, specifically 10 classes corresponding to branches of computer science, with very high connectivity. The node features are derived from the text of the corresponding articles. They were calculated as the average of pretrained GloVe word embeddings (Pennington et al., 2014), resulting in 300-dimensional node features. </p> <p class="description-stats"> 89 <span class="smaller-text">PAPERS</span> • 2 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/orkut"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-color: #90B06D;opacity: 0.3;"> <span class="dataset-img-icon"><span class=" icon-wrapper icon-fa icon-fa-duotone" data-name=""></span></span> <span class="dataset-img-text">Orkut</span> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> Orkut </span> <div class="description"> <p> Orkut is a social network dataset consisting of friendship social network and ground-truth communities from Orkut.com on-line social network where users form friendship each other. </p> <p class="description-stats"> 81 <span class="smaller-text">PAPERS</span> • <span class="smaller-text">NO BENCHMARKS YET</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/reddit-5k"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-color: #90B06D;opacity: 0.3;"> <span class="dataset-img-icon"><span class=" icon-wrapper icon-fa icon-fa-duotone" data-name=""></span></span> <span class="dataset-img-text">REDDIT-5K</span> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> REDDIT-5K <span class="full-name">(REDDIT-MULTI-5K)</span> </span> <div class="description"> <p> Reddit-5K is a relational dataset extracted from Reddit. </p> <p class="description-stats"> 78 <span class="smaller-text">PAPERS</span> • 1 <span class="smaller-text">BENCHMARK</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/nci109"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/e0aaec8d-322a-4c0a-9d58-a32af4144796.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> NCI109 </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/ea183aac-c05c-4c21-84b7-b388be50ce52.jpg"> <p> Tudataset: A collection of benchmark datasets for learning with graphs </p> <p class="description-stats"> 76 <span class="smaller-text">PAPERS</span> • 1 <span class="smaller-text">BENCHMARK</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/aminer"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-color: #90B06D;opacity: 0.3;"> <span class="dataset-img-icon"><span class=" icon-wrapper icon-fa icon-fa-duotone" data-name=""></span></span> <span class="dataset-img-text">AMiner</span> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> AMiner </span> <div class="description"> <p> The AMiner Dataset is a collection of different relational datasets. It consists of a set of relational networks such as citation networks, academic social networks or topic-paper-autor networks among others. </p> <p class="description-stats"> 74 <span class="smaller-text">PAPERS</span> • 1 <span class="smaller-text">BENCHMARK</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/radgraph"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-color: #CD933C;opacity: 0.3;"> <span class="dataset-img-icon"><span class=" icon-wrapper icon-fa icon-fa-duotone" data-name=""></span></span> <span class="dataset-img-text">RadGraph</span> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> RadGraph <span class="full-name">(RadGraph: Extracting Clinical Entities and Relations from Radiology Reports)</span> </span> <div class="description"> <p> RadGraph is a dataset of entities and relations in radiology reports based on our novel information extraction schema, consisting of 600 reports with 30K radiologist annotations and 221K reports with 10.5M automatically generated annotations. </p> <p class="description-stats"> 69 <span class="smaller-text">PAPERS</span> • <span class="smaller-text">NO BENCHMARKS YET</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/moses"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-color: #90B06D;opacity: 0.3;"> <span class="dataset-img-icon"><span class=" icon-wrapper icon-fa icon-fa-duotone" data-name=""></span></span> <span class="dataset-img-text">MOSES</span> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> MOSES <span class="full-name">(Molecular sets (MOSES))</span> </span> <div class="description"> <p> The set is based on the ZINC Clean Leads collection. It contains 4,591,276 molecules in total, filtered by molecular weight in the range from 250 to 350 Daltons, a number of rotatable bonds not greater than 7, and XlogP less than or equal to 3.5. We removed molecules containing charged atoms or atoms besides C, N, S, O, F, Cl, Br, H or cycles longer than 8 atoms. The molecules were filtered via medicinal chemistry filters (MCFs) and PAINS filters. </p> <p class="description-stats"> 65 <span class="smaller-text">PAPERS</span> • 1 <span class="smaller-text">BENCHMARK</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/reuters-21578"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-color: #90B06D;opacity: 0.3;"> <span class="dataset-img-icon"><span class=" icon-wrapper icon-fa icon-fa-duotone" data-name=""></span></span> <span class="dataset-img-text">Reuters-21578</span> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> Reuters-21578 </span> <div class="description"> <p> The Reuters-21578 dataset is a collection of documents with news articles. The original corpus has 10,369 documents and a vocabulary of 29,930 words. </p> <p class="description-stats"> 65 <span class="smaller-text">PAPERS</span> • 6 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/friendster"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-color: #90B06D;opacity: 0.3;"> <span class="dataset-img-icon"><span class=" icon-wrapper icon-fa icon-fa-duotone" data-name=""></span></span> <span class="dataset-img-text">Friendster</span> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> Friendster </span> <div class="description"> <p> Friendster is an on-line gaming network. Before re-launching as a game website, Friendster was a social networking site where users can form friendship edge each other. Friendster social network also allows users form a group which other members can then join. The Friendster dataset consist of ground-truth communities (based on user-defined groups) and the social network from induced subgraph of the nodes that either belong to at least one community or are connected to other nodes that belong to at least one community. </p> <p class="description-stats"> 63 <span class="smaller-text">PAPERS</span> • <span class="smaller-text">NO BENCHMARKS YET</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/pascalvoc-sp"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/63bc34d7-55d3-44d1-aafd-60f976699508.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> Long Range Graph Benchmark (LRGB) </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/f92509b7-3443-4f6f-8d34-d6b271152e19.jpg"> <p> The Long Range Graph Benchmark (LRGB) is a collection of 5 graph learning datasets that arguably require long-range reasoning to achieve strong performance in a given task. The 5 datasets in this benchmark can be used to prototype new models that can capture long range dependencies in graphs. </p> <p class="description-stats"> 62 <span class="smaller-text">PAPERS</span> • 5 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/aids"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000001921-9686461f_55YyvOm.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> AIDS </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000001921-f7b7359d.jpg"> <p> AIDS is a graph dataset. It consists of 2000 graphs representing molecular compounds which are constructed from the AIDS Antiviral Screen Database of Active Compounds. It contains 4395 chemical compounds, of which 423 belong to class CA, 1081 to CM, and the remaining compounds to CI. </p> <p class="description-stats"> 58 <span class="smaller-text">PAPERS</span> • 1 <span class="smaller-text">BENCHMARK</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/slashdot"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-color: #90B06D;opacity: 0.3;"> <span class="dataset-img-icon"><span class=" icon-wrapper icon-fa icon-fa-duotone" data-name=""></span></span> <span class="dataset-img-text">Slashdot</span> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> Slashdot </span> <div class="description"> <p> The Slashdot dataset is a relational dataset obtained from Slashdot. Slashdot is a technology-related news website know for its specific user community. The website features user-submitted and editor-evaluated current primarily technology oriented news. In 2002 Slashdot introduced the Slashdot Zoo feature which allows users to tag each other as friends or foes. The network cotains friend/foe links between the users of Slashdot. The network was obtained in February 2009. </p> <p class="description-stats"> 56 <span class="smaller-text">PAPERS</span> • 2 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/penn94"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-color: #90B06D;opacity: 0.3;"> <span class="dataset-img-icon"><span class=" icon-wrapper icon-fa icon-fa-duotone" data-name=""></span></span> <span class="dataset-img-text">Penn94</span> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> Penn94 </span> <div class="description"> <p> Node classification on Penn94 </p> <p class="description-stats"> 55 <span class="smaller-text">PAPERS</span> • 2 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/epinions"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-color: #90B06D;opacity: 0.3;"> <span class="dataset-img-icon"><span class=" icon-wrapper icon-fa icon-fa-duotone" data-name=""></span></span> <span class="dataset-img-text">Epinions</span> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> Epinions </span> <div class="description"> <p> The Epinions dataset is built form a who-trust-whom online social network of a general consumer review site Epinions.com. Members of the site can decide whether to ''trust'' each other. All the trust relationships interact and form the Web of Trust which is then combined with review ratings to determine which reviews are shown to the user. It contains 75,879 nodes and 50,8837 edges. </p> <p class="description-stats"> 53 <span class="smaller-text">PAPERS</span> • 2 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/gap"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-color: #90B06D;opacity: 0.3;"> <span class="dataset-img-icon"><span class=" icon-wrapper icon-fa icon-fa-duotone" data-name=""></span></span> <span class="dataset-img-text">GAP</span> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> GAP <span class="full-name">(GAP Benchmark Suite)</span> </span> <div class="description"> <p> GAP is a graph processing benchmark suite with the goal of helping to standardize graph processing evaluations. Fewer differences between graph processing evaluations will make it easier to compare different research efforts and quantify improvements. The benchmark not only specifies graph kernels, input graphs, and evaluation methodologies, but it also provides optimized baseline implementations. These baseline implementations are representative of state-of-the-art performance, and thus new contributions should outperform them to demonstrate an improvement. The input graphs are sized appropriately for shared memory platforms, but any implementation on any platform that conforms to the benchmark's specifications could be compared. This benchmark suite can be used in a variety of settings. Graph framework developers can demonstrate the generality of their programming model by implementing all of the benchmark's kernels and delivering competitive performance on all of the benchmark's gra </p> <p class="description-stats"> 53 <span class="smaller-text">PAPERS</span> • 1 <span class="smaller-text">BENCHMARK</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/bio"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-color: #CD933C;opacity: 0.3;"> <span class="dataset-img-icon"><span class=" icon-wrapper icon-fa icon-fa-duotone" data-name=""></span></span> <span class="dataset-img-text">Bio</span> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> Bio <span class="full-name">(Bio AMR Corpus)</span> </span> <div class="description"> <p> This corpus includes annotations of cancer-related PubMed articles, covering 3 full papers (PMID:24651010, PMID:11777939, PMID:15630473) as well as the result sections of 46 additional PubMed papers. The corpus also includes about 1000 sentences each from the BEL BioCreative training corpus and the Chicago Corpus. </p> <p class="description-stats"> 52 <span class="smaller-text">PAPERS</span> • 2 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/mmkg"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-color: #A395C6;opacity: 0.3;"> <span class="dataset-img-icon"><span class=" icon-wrapper icon-fa icon-fa-duotone" data-name=""></span></span> <span class="dataset-img-text">MMKG</span> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> MMKG </span> <div class="description"> <p> MMKG is a collection of three knowledge graphs for link prediction and entity matching research. Contrary to other knowledge graph datasets, these knowledge graphs contain both numerical features and images for all entities as well as entity alignments between pairs of KGs. While MMKG is intended to perform relational reasoning across different entities and images, previous resources are intended to perform visual reasoning within the same image. </p> <p class="description-stats"> 46 <span class="smaller-text">PAPERS</span> • 5 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/amazon-product-data"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-color: #CD933C;opacity: 0.3;"> <span class="dataset-img-icon"><span class=" icon-wrapper icon-fa icon-fa-duotone" data-name=""></span></span> <span class="dataset-img-text">Amazon Product Data</span> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> Amazon Product Data </span> <div class="description"> <p> This dataset contains product reviews and metadata from Amazon, including 142.8 million reviews spanning May 1996 - July 2014. </p> <p class="description-stats"> 38 <span class="smaller-text">PAPERS</span> • 6 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/genius"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-color: #90B06D;opacity: 0.3;"> <span class="dataset-img-icon"><span class=" icon-wrapper icon-fa icon-fa-duotone" data-name=""></span></span> <span class="dataset-img-text">genius</span> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> genius </span> <div class="description"> <p> node classification on genius </p> <p class="description-stats"> 37 <span class="smaller-text">PAPERS</span> • 2 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> </div> <nav class="datasets-nav" aria-label="Page navigation"> <ul class="pagination 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